Community-Wide Assessment of Intensive Care Outcomes Using a Physiologically Based Prognostic Measure: Data

6Aug 2014

Physiologic variables with missing data were assumed to have normal values, although a minimum of nine physiologic measurements were required for inclusion in the analysis. Other data elements included the following: gender; primary diagnosis prompting admission to the ICU, as classified according to a prior taxono-my; admission source, based on eight mutually exclusive categories (eg, emergency department, operating room, other hospital ward); dates of ICU and hospital admission and discharge; vital status at ICU and hospital discharge (ie, alive/dead); discharge destination (eg, home, skilled nursing facility, other acute-care hospital); and ICU and hospital length of stay. As previously reported, several steps were taken to ensure the reliability of study data, including explicit definitions of each variable that were consistent with prior applications of the APACHE method, formal training sessions for data abstractors, manual and electronic edits to identify out-of-range or discrepant data (eg, patients with a diagnosis of shock and normal vital signs), and independent reabstraction of data for randomly selected patients from each hospital to determine interrater reliability.Severity of Illness Measurement of admission severity of illness was based on the APACHE III method Click Here purchase zyrtec. For each patient, an APACHE III APS was determined on the basis of age, comorbidity, and physiologic abnormalities. Scores have a possible range of 0 to 299 and were determined using previously validated weights for each vari-able. A predicted risk of in-hospital death (0 to 100%) was then determined using a previously developed multivariable equation that considered the APS score, admission source, and ICU diagnosis. Variable weightings used in the equation were derived from an analysis of 16,622 ICU admissions in 1988 and 1989 to 40 US hospitals, in which the rate of in-hospital death was 16.5%. Thus, the risk predictions provided an expected probability of death, based on the prior national normative sample, and an external benchmark to which the study hospitals could be compared.